{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PersistentDataset, CacheDataset, and simple Dataset Tutorial and Speed Test\n",
    "\n",
    "This tutorial shows how to accelerate PyTorch medical DL program based on\n",
    "how data is loaded and preprocessed using different MONAI `Dataset` managers.\n",
    "\n",
    "`Dataset` provides the simplest model of data loading.  Each time a dataset is needed, it is reloaded from the original datasources, and processed through the all non-random and random transforms to generate analyzable tensors. This mechanism has the smallest memory footprint, and the smallest temporary disk footprint.\n",
    "\n",
    "`CacheDataset` provides a mechanism to pre-load all original data and apply non-random transforms into analyzable tensors loaded in memory prior to starting analysis.  The `CacheDataset` requires all tensor representations of data requested to be loaded into memory at once. The subset of random transforms are applied to the cached components before use. This is the highest performance dataset if all data fits in core memory.\n",
    "\n",
    "`PersistentDataset` processes original data sources through the non-random transforms on first use, and stores these intermediate tensor values to an on-disk persistence representation.  The intermediate processed tensors are loaded from disk on each use for processing by the random-transforms for each analysis request.  The `PersistentDataset` has a similar memory footprint to the simple `Dataset`, with performance characterisics close to the `CacheDataset` at the expense of disk storage.  Additially, the cost of first time processing of data is distributed across each first use.\n",
    "\n",
    "It's modified from the Spleen 3D segmentation tutorial notebook.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/persistent_dataset_speed.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -qU \"monai[gdown, nibabel]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -qU matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import pathlib\n",
    "import shutil\n",
    "import tempfile\n",
    "import time\n",
    "\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, Dataset, PersistentDataset, list_data_collate\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import compute_meandice\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.transforms import (\n",
    "    AddChanneld,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    LoadNiftid,\n",
    "    Orientationd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    ToTensord,\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_process(train_ds, val_ds):\n",
    "    # use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "    # to generate 2 x 4 images for network training\n",
    "    train_loader = torch.utils.data.DataLoader(\n",
    "        train_ds, batch_size=2, shuffle=True, num_workers=4, collate_fn=list_data_collate,\n",
    "    )\n",
    "    val_loader = torch.utils.data.DataLoader(val_ds, batch_size=1, num_workers=4)\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    model = UNet(\n",
    "        dimensions=3,\n",
    "        in_channels=1,\n",
    "        out_channels=2,\n",
    "        channels=(16, 32, 64, 128, 256),\n",
    "        strides=(2, 2, 2, 2),\n",
    "        num_res_units=2,\n",
    "        norm=Norm.BATCH,\n",
    "    ).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "\n",
    "    epoch_num = 600\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    epoch_loss_values = list()\n",
    "    metric_values = list()\n",
    "    epoch_times = list()\n",
    "    total_start = time.time()\n",
    "    for epoch in range(epoch_num):\n",
    "        epoch_start = time.time()\n",
    "        print(\"-\" * 10)\n",
    "        print(f\"epoch {epoch + 1}/{epoch_num}\")\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "        for batch_data in train_loader:\n",
    "            step_start = time.time()\n",
    "            step += 1\n",
    "            inputs, labels = (\n",
    "                batch_data[\"image\"].to(device),\n",
    "                batch_data[\"label\"].to(device),\n",
    "            )\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = loss_function(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            print(\n",
    "                f\"{step}/{len(train_ds) // train_loader.batch_size}, train_loss: {loss.item():.4f}\"\n",
    "                f\" step time: {(time.time() - step_start):.4f}\"\n",
    "            )\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                metric_sum = 0.0\n",
    "                metric_count = 0\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = (\n",
    "                        val_data[\"image\"].to(device),\n",
    "                        val_data[\"label\"].to(device),\n",
    "                    )\n",
    "                    roi_size = (160, 160, 160)\n",
    "                    sw_batch_size = 4\n",
    "                    val_outputs = sliding_window_inference(\n",
    "                        val_inputs, roi_size, sw_batch_size, model\n",
    "                    )\n",
    "                    value = compute_meandice(\n",
    "                        y_pred=val_outputs,\n",
    "                        y=val_labels,\n",
    "                        include_background=False,\n",
    "                        to_onehot_y=True,\n",
    "                        mutually_exclusive=True,\n",
    "                    )\n",
    "                    metric_count += len(value)\n",
    "                    metric_sum += value.sum().item()\n",
    "                metric = metric_sum / metric_count\n",
    "                metric_values.append(metric)\n",
    "                if metric > best_metric:\n",
    "                    best_metric = metric\n",
    "                    best_metric_epoch = epoch + 1\n",
    "                    torch.save(\n",
    "                        model.state_dict(), os.path.join(root_dir, \"best_metric_model.pth\"),\n",
    "                    )\n",
    "                    print(\"saved new best metric model\")\n",
    "                print(\n",
    "                    f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "                    f\" best mean dice: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
    "                )\n",
    "        print(f\"time consuming of epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}\")\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "\n",
    "        IPython.display.clear_output()\n",
    "\n",
    "    print(\n",
    "        f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
    "        f\" total time: {(time.time() - total_start):.4f}\"\n",
    "    )\n",
    "    return (\n",
    "        epoch_num,\n",
    "        time.time() - total_start,\n",
    "        epoch_loss_values,\n",
    "        metric_values,\n",
    "        epoch_times,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of speed testing\n",
    "\n",
    "The `PersistenceDataset`, `CacheDataset`, and `Dataset` are compared for speed for running 600 epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the dataset.\n",
    "The dataset comes from http://medicaldecathlon.com/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "resource = \"https://drive.google.com/uc?id=1jzeNU1EKnK81PyTsrx0ujfNl-t0Jo8uE\"\n",
    "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
    "data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
    "\n",
    "download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = sorted(glob.glob(os.path.join(data_dir, \"imagesTr\", \"*.nii.gz\")))\n",
    "train_labels = sorted(glob.glob(os.path.join(data_dir, \"labelsTr\", \"*.nii.gz\")))\n",
    "data_dicts = [\n",
    "    {\"image\": image_name, \"label\": label_name}\n",
    "    for image_name, label_name in zip(train_images, train_labels)\n",
    "]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation\n",
    "\n",
    "Deterministic transforms during training:\n",
    "* LoadNiftid\n",
    "* AddChanneld\n",
    "* Spacingd\n",
    "* Orientationd\n",
    "* ScaleIntensityRanged\n",
    "\n",
    "Non-deterministic transforms:\n",
    "* RandCropByPosNegLabeld\n",
    "* ToTensord\n",
    "\n",
    "All the validation transforms are deterministic.\n",
    "The results of all the deterministic transforms will be cached to accelerate training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformations():\n",
    "    train_transforms = Compose(\n",
    "        [\n",
    "            LoadNiftid(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            # randomly crop out patch samples from big image based on pos / neg ratio\n",
    "            # the image centers of negative samples must be in valid image area\n",
    "            RandCropByPosNegLabeld(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                label_key=\"label\",\n",
    "                spatial_size=(96, 96, 96),\n",
    "                pos=1,\n",
    "                neg=1,\n",
    "                num_samples=4,\n",
    "                image_key=\"image\",\n",
    "                image_threshold=0,\n",
    "            ),\n",
    "            ToTensord(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # NOTE: No random cropping in the validation data, we will evaluate the entire image using a sliding window.\n",
    "    val_transforms = Compose(\n",
    "        [\n",
    "            LoadNiftid(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            ToTensord(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    return train_transforms, val_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic training and regular `Dataset`\n",
    "\n",
    "Load each original dataset and transform each time it is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "train_trans, val_trans = transformations()\n",
    "train_ds = Dataset(data=train_files, transform=train_trans)\n",
    "val_ds = Dataset(data=val_files, transform=val_trans)\n",
    "\n",
    "(epoch_num, total_time, epoch_loss_values, metric_values, epoch_times,) = train_process(\n",
    "    train_ds, val_ds\n",
    ")\n",
    "print(f\"total training time of {epoch_num} epochs with regular Dataset: {total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic training and `PersistentDataset`\n",
    "\n",
    "Use persistent storage of non-random transformed training and validation data computed once and stored in persistently across runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "persistent_cache = pathlib.Path(root_dir, \"persistent_cache\")\n",
    "persistent_cache.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "set_determinism(seed=0)\n",
    "train_trans, val_trans = transformations()\n",
    "train_persitence_ds = PersistentDataset(\n",
    "    data=train_files, transform=train_trans, cache_dir=persistent_cache\n",
    ")\n",
    "val_persitence_ds = PersistentDataset(\n",
    "    data=val_files, transform=val_trans, cache_dir=persistent_cache\n",
    ")\n",
    "\n",
    "(\n",
    "    persistence_epoch_num,\n",
    "    persistence_total_time,\n",
    "    persistence_epoch_loss_values,\n",
    "    persistence_metric_values,\n",
    "    persistence_epoch_times,\n",
    ") = train_process(train_persitence_ds, val_persitence_ds)\n",
    "print(\n",
    "    f\"total training time of {persistence_epoch_num}\"\n",
    "    f\" epochs with persistent storage Dataset: {persistence_total_time:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic training and `CacheDataset`\n",
    "\n",
    "Precompute all non-random transforms of original data and store in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "train_trans, val_trans = transformations()\n",
    "cache_init_start = time.time()\n",
    "cache_train_ds = CacheDataset(\n",
    "    data=train_files, transform=train_trans, cache_rate=1.0, num_workers=4\n",
    ")\n",
    "cache_val_ds = CacheDataset(data=val_files, transform=val_trans, cache_rate=1.0, num_workers=4)\n",
    "cache_init_time = time.time() - cache_init_start\n",
    "\n",
    "(\n",
    "    cache_epoch_num,\n",
    "    cache_total_time,\n",
    "    cache_epoch_loss_values,\n",
    "    cache_metric_values,\n",
    "    cache_epoch_times,\n",
    ") = train_process(cache_train_ds, cache_val_ds)\n",
    "print(f\"total training time of {cache_epoch_num} epochs with CacheDataset: {cache_total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 18))\n",
    "\n",
    "plt.subplot(3, 2, 1)\n",
    "plt.title(\"Regular Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(3, 2, 2)\n",
    "plt.title(\"Regular Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = cache_metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(3, 2, 3)\n",
    "plt.title(\"PersistentDataset Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = persistence_epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"blue\")\n",
    "\n",
    "plt.subplot(3, 2, 4)\n",
    "plt.title(\"PersistentDataset Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = persistence_metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"blue\")\n",
    "\n",
    "plt.subplot(3, 2, 5)\n",
    "plt.title(\"Cache Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = cache_epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "\n",
    "plt.subplot(3, 2, 6)\n",
    "plt.title(\"Cache Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = cache_metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time and every epoch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Train Time(600 epochs)\")\n",
    "plt.bar(\"regular\", total_time, 1, label=\"Regular Dataset\", color=\"red\")\n",
    "plt.bar(\n",
    "    \"persistent\", persistence_total_time, 1, label=\"Persistent Dataset\", color=\"blue\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"cache\", cache_init_time + cache_total_time, 1, label=\"Cache Dataset\", color=\"green\",\n",
    ")\n",
    "plt.bar(\"cache\", cache_init_time, 1, label=\"Cache Init\", color=\"orange\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Epoch Time\")\n",
    "x = [i + 1 for i in range(len(epoch_times))]\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.plot(x, epoch_times, label=\"Regular Dataset\", color=\"red\")\n",
    "plt.plot(x, persistence_epoch_times, label=\"Persistent Dataset\", color=\"blue\")\n",
    "plt.plot(x, cache_epoch_times, label=\"Cache Dataset\", color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
